{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:40:23Z","timestamp":1774554023634,"version":"3.50.1"},"reference-count":96,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971086"],"award-info":[{"award-number":["61971086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00530-024-01619-y","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T18:38:27Z","timestamp":1734374307000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Stochastic stylization transformer with self-supervision for iris recognition"],"prefix":"10.1007","volume":"31","author":[{"given":"Lingyao","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingbing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"1619_CR1","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1109\/TIFS.2022.3208812","volume":"17","author":"MJ Lee","year":"2022","unstructured":"Lee, M.J., Jin, Z., Liang, S.-N., Tistarelli, M.: Alignment-robust cancelable biometric scheme for iris verification. IEEE Trans. Inf. Forensic Secur. 17, 3449\u20133464 (2022)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"2","key":"1619_CR2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00530-024-01280-5","volume":"30","author":"Y Yan","year":"2024","unstructured":"Yan, Y., Wang, Q., Zhu, H., Jiang, W.: Iris-lahnet: a lightweight attention-guided high-resolution network for iris segmentation and localization. Multimed Syst. 30(2), 85 (2024)","journal-title":"Multimed Syst."},{"key":"1619_CR3","doi-asserted-by":"crossref","unstructured":"Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M., Zhang, D.: Biometrics recognition using deep learning: A survey. Artif. Intell. Rev. pp 1\u201349 (2023)","DOI":"10.1007\/s10462-022-10237-x"},{"issue":"9","key":"1619_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3651306","volume":"56","author":"K Nguyen","year":"2024","unstructured":"Nguyen, K., Proen\u00e7a, H., Alonso-Fernandez, F.: Deep learning for iris recognition: a survey. ACM Comput. Surv. 56(9), 1\u201335 (2024)","journal-title":"ACM Comput. Surv."},{"issue":"11","key":"1619_CR5","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/34.244676","volume":"15","author":"JG Daugman","year":"1993","unstructured":"Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148\u20131161 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1619_CR6","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s00530-024-01306-y","volume":"30","author":"C-Y Chang","year":"2024","unstructured":"Chang, C.-Y., Santra, A.S., Chang, I.-H., Wu, S.-J., Roy, D.S., Zhang, Q.: Design and implementation of a real-time face recognition system based on artificial intelligence techniques. Multimed Syst. 30(2), 114 (2024)","journal-title":"Multimed Syst."},{"issue":"1","key":"1619_CR7","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","volume":"14","author":"AK Jain","year":"2004","unstructured":"Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4\u201320 (2004)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1619_CR8","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.1109\/TIP.2020.2999211","volume":"29","author":"K Nguyen","year":"2020","unstructured":"Nguyen, K., Fookes, C., Sridharan, S.: Constrained design of deep iris networks. IEEE Trans. Image Process. 29, 7166\u20137175 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"1619_CR9","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1109\/TCSVT.2021.3117291","volume":"32","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, Y., Li, Y., He, R., Sun, Z.: Cross-spectral iris recognition by learning device-specific band. IEEE Trans. Circuits Syst. Video Technol. 32(6), 3810\u20133824 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"1619_CR10","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TPAMI.2022.3152857","volume":"45","author":"K Nguyen","year":"2023","unstructured":"Nguyen, K., Fookes, C., Sridharan, S., Ross, A.: Complex-valued iris recognition network. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 182\u2013196 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR11","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TIFS.2022.3154240","volume":"17","author":"J Wei","year":"2022","unstructured":"Wei, J., Huang, H., Wang, Y., He, R., Sun, Z.: Towards more discriminative and robust iris recognition by learning uncertain factors. IEEE Trans. Inf. Forensic Secur. 17, 865\u2013879 (2022)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"12","key":"1619_CR12","first-page":"2211","volume":"31","author":"Z Sun","year":"2008","unstructured":"Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211\u20132226 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR13","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.patrec.2015.09.002","volume":"82","author":"N Othman","year":"2016","unstructured":"Othman, N., Dorizzi, B., Garcia-Salicetti, S.: Osiris: an open source iris recognition software. Pattern Recognit. Lett. 82, 124\u2013131 (2016)","journal-title":"Pattern Recognit. Lett."},{"issue":"1","key":"1619_CR14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TIFS.2016.2606083","volume":"12","author":"Y Hu","year":"2016","unstructured":"Hu, Y., Sirlantzis, K., Howells, G.: Optimal generation of iris codes for iris recognition. IEEE Trans. Inf. Forensic Secur. 12(1), 157\u2013171 (2016)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR15","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al: An image is worth 16x16 words: Transformers for image recognition at scale. In: Int. Conf. Learn. Represent. pp. 1\u201321 (2020)"},{"key":"1619_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Int. Conf. Comput. Vis. pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1619_CR17","first-page":"30008","volume":"34","author":"J Yang","year":"2021","unstructured":"Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., Gao, J.: Focal attention for long-range interactions in vision transformers. Adv. Neural Inform. Process. Syst. 34, 30008\u201330022 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1619_CR18","doi-asserted-by":"crossref","unstructured":"Ren, S., Zhou, D., He, S., Feng, J., Wang, X.: Shunted self-attention via multi-scale token aggregation. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 10853\u201310862 (2022)","DOI":"10.1109\/CVPR52688.2022.01058"},{"key":"1619_CR19","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neucom.2017.12.053","volume":"330","author":"M Zhang","year":"2019","unstructured":"Zhang, M., He, Z., Zhang, H., Tan, T., Sun, Z.: Toward practical remote iris recognition: a boosting based framework. Neurocomputing 330, 238\u2013252 (2019)","journal-title":"Neurocomputing"},{"issue":"3","key":"1619_CR20","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1049\/iet-bmt.2018.5199","volume":"8","author":"J Daugman","year":"2019","unstructured":"Daugman, J., Downing, C.: Radial correlations in iris patterns, and mutual information within iriscodes. IET Biom. 8(3), 185\u2013189 (2019)","journal-title":"IET Biom."},{"issue":"2","key":"1619_CR21","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TIFS.2015.2500196","volume":"11","author":"J Daugman","year":"2015","unstructured":"Daugman, J.: Information theory and the iriscode. IEEE Trans. Inf. Forensic Secur. 11(2), 400\u2013409 (2015)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR22","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TIFS.2022.3221897","volume":"18","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, Y., Huang, H., He, R., Sun, Z., Gao, X.: Contextual measures for iris recognition. IEEE Trans. Inf. Forensic Secur. 18, 57\u201370 (2022)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"28","key":"1619_CR23","doi-asserted-by":"crossref","first-page":"21071","DOI":"10.1007\/s00521-023-08800-w","volume":"35","author":"L Jia","year":"2023","unstructured":"Jia, L., Sun, Q., Li, P.: Structure correlation-aware attention for iris recognition. Neural Comput. Appl. 35(28), 21071\u201321091 (2023)","journal-title":"Neural Comput. Appl."},{"key":"1619_CR24","doi-asserted-by":"crossref","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., Li, Y.: Maxvit: Multi-axis vision transformer. In: Eur. Conf. Comput. Vis. pp. 459\u2013479 (2022)","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"1619_CR25","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L.: Cvt: Introducing convolutions to vision transformers. In: Int. Conf. Comput. Vis. pp. 22\u201331 (2021)","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"1619_CR26","doi-asserted-by":"crossref","unstructured":"Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., Xu, C.: Cmt: Convolutional neural networks meet vision transformers. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 12175\u201312185 (2022)","DOI":"10.1109\/CVPR52688.2022.01186"},{"issue":"11","key":"1619_CR27","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","volume":"43","author":"L Jing","year":"2021","unstructured":"Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4037\u20134058 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1619_CR29","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill, J.-B., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural Inform. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1619_CR30","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Int. Conf. Comput. Vis. pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"1619_CR31","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.patcog.2018.08.010","volume":"86","author":"K Wang","year":"2019","unstructured":"Wang, K., Kumar, A.: Cross-spectral iris recognition using cnn and supervised discrete hashing. Pattern Recognit. 86, 85\u201398 (2019)","journal-title":"Pattern Recognit."},{"key":"1619_CR32","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIFS.2020.2980791","volume":"15","author":"C Wang","year":"2020","unstructured":"Wang, C., Muhammad, J., Wang, Y., He, Z., Sun, Z.: Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition. IEEE Trans. Inf. Forensic Secur. 15, 2944\u20132959 (2020)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR33","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/LSP.2021.3079850","volume":"28","author":"Z Luo","year":"2021","unstructured":"Luo, Z., Li, J., Zhu, Y.: A deep feature fusion network based on multiple attention mechanisms for joint iris-periocular biometric recognition. IEEE Signal Process. Lett. 28, 1060\u20131064 (2021)","journal-title":"IEEE Signal Process. Lett."},{"issue":"10","key":"1619_CR34","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/TIFS.2017.2686013","volume":"12","author":"N Liu","year":"2017","unstructured":"Liu, N., Liu, J., Sun, Z., Tan, T.: A code-level approach to heterogeneous iris recognition. IEEE Trans. Inf. Forensic Secur. 12(10), 2373\u20132386 (2017)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR35","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Yang, J., Lattas, A., Zafeiriou, S.: Variational prototype learning for deep face recognition. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 11906\u201311915 (2021)","DOI":"10.1109\/CVPR46437.2021.01173"},{"issue":"15","key":"1619_CR36","doi-asserted-by":"crossref","first-page":"11477","DOI":"10.1007\/s00500-019-04610-2","volume":"24","author":"M Choudhary","year":"2020","unstructured":"Choudhary, M., Tiwari, V., Venkanna, U.: Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models. Soft. Comput. 24(15), 11477\u201311491 (2020)","journal-title":"Soft. Comput."},{"key":"1619_CR37","doi-asserted-by":"crossref","unstructured":"Jalilian, E., Wimmer, G., Uhl, A., Karakaya, M.: Deep learning based off-angle iris recognition. In: IEEE Int. Conf. Acoust. Speech Signal Process. Proc. pp. 4048\u20134052 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746090"},{"key":"1619_CR38","doi-asserted-by":"crossref","unstructured":"Khan, S.K., Tinsley, P., Czajka, A.: Deformirisnet: An identity-preserving model of iris texture deformation. In: Proc. - IEEE Winter Conf. Appl. Comput. Vis. pp. 900\u2013908 (2023)","DOI":"10.1109\/WACV56688.2023.00096"},{"issue":"1","key":"1619_CR39","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1049\/iet-bmt.2018.5146","volume":"8","author":"E Ribeiro","year":"2019","unstructured":"Ribeiro, E., Uhl, A., Alonso-Fernandez, F.: Iris super-resolution using cnns: is photo-realism important to iris recognition? IET Biom. 8(1), 69\u201378 (2019)","journal-title":"IET Biom."},{"key":"1619_CR40","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, Q., Huang, H., Zheng, X., He, Z.: Adversarial iris super resolution. In: Int. Conf. Biom. pp. 1\u20138 (2019)","DOI":"10.1109\/ICB45273.2019.8987243"},{"key":"1619_CR41","doi-asserted-by":"crossref","unstructured":"Hu, Y., Sirlantzis, K., Howells, G.: A study on iris textural correlation using steering kernels. In: IEEE Int. Conf. Biometrics: Theory, Appl. Syst. pp. 1\u20138 (2016)","DOI":"10.1109\/BTAS.2016.7791160"},{"issue":"12","key":"1619_CR42","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1109\/TIFS.2019.2913234","volume":"14","author":"K Wang","year":"2019","unstructured":"Wang, K., Kumar, A.: Toward more accurate iris recognition using dilated residual features. IEEE Trans. Inf. Forensic Secur. 14(12), 3233\u20133245 (2019)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR43","doi-asserted-by":"crossref","unstructured":"Yang, G., Zeng, H., Li, P., Zhang, L.: High-order information for robust iris recognition under less controlled conditions. In: IEEE Int. Conf. Image Process. pp. 4535\u20134539 (2015)","DOI":"10.1109\/ICIP.2015.7351665"},{"issue":"10","key":"1619_CR44","doi-asserted-by":"crossref","first-page":"11273","DOI":"10.1007\/s10489-021-02925-y","volume":"52","author":"L Jia","year":"2022","unstructured":"Jia, L., Shi, X., Sun, Q., Tang, X., Li, P.: Second-order convolutional networks for iris recognition. Appl. Intell. 52(10), 11273\u201311287 (2022)","journal-title":"Appl. Intell."},{"key":"1619_CR45","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/TIFS.2020.3023289","volume":"16","author":"K Wang","year":"2020","unstructured":"Wang, K., Kumar, A.: Periocular-assisted multi-feature collaboration for dynamic iris recognition. IEEE Trans. Inf. Forensic Secur. 16, 866\u2013879 (2020)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR46","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neunet.2019.11.009","volume":"122","author":"S Umer","year":"2020","unstructured":"Umer, S., Sardar, A., Dhara, B.C., Rout, R.K., Pandey, H.M.: Person identification using fusion of iris and periocular deep features. Neural Netw. 122, 407\u2013419 (2020)","journal-title":"Neural Netw."},{"key":"1619_CR47","first-page":"22732","volume":"34","author":"Y Xu","year":"2021","unstructured":"Xu, Y., Li, F., Chen, Z., Liang, J., Quan, Y.: Encoding spatial distribution of convolutional features for texture representation. Adv. Neural Inform. Process. Syst. 34, 22732\u201322744 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"issue":"1","key":"1619_CR48","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/TPAMI.2023.3325230","volume":"46","author":"W Zhai","year":"2024","unstructured":"Zhai, W., Cao, Y., Zhang, J., Xie, H., Tao, D., Zha, Z.-J.: On exploring multiplicity of primitives and attributes for texture recognition in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 46(1), 403\u2013420 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR49","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Kumar, A.: Towards more accurate iris recognition using deeply learned spatially corresponding features. In: Int. Conf. Comput. Vis. pp. 3809\u20133818 (2017)","DOI":"10.1109\/ICCV.2017.411"},{"key":"1619_CR50","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.patcog.2019.04.010","volume":"93","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Kumar, A.: A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features. Pattern Recognit. 93, 546\u2013557 (2019)","journal-title":"Pattern Recognit."},{"issue":"5802","key":"1619_CR51","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/290091a0","volume":"290","author":"B Julesz","year":"1981","unstructured":"Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290(5802), 91\u201397 (1981)","journal-title":"Nature"},{"issue":"1","key":"1619_CR52","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s11263-018-1125-z","volume":"127","author":"L Liu","year":"2019","unstructured":"Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., Pietik\u00e4inen, M.: From bow to cnn: Two decades of texture representation for texture classification. Int. J. Comput. Vis. 127(1), 74\u2013109 (2019)","journal-title":"Int. J. Comput. Vis."},{"issue":"5","key":"1619_CR53","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","volume":"67","author":"RM Haralick","year":"1979","unstructured":"Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786\u2013804 (1979)","journal-title":"Proc. IEEE"},{"key":"1619_CR54","doi-asserted-by":"crossref","unstructured":"Tang, X., Xie, J., Li, P.: Deep convolutional features for iris recognition. In: Chin. Conf. Biom. Recog. pp. 391\u2013400 (2017)","DOI":"10.1007\/978-3-319-69923-3_42"},{"key":"1619_CR55","doi-asserted-by":"crossref","unstructured":"Yang, K., Xu, Z., Fei, J.: Dualsanet: Dual spatial attention network for iris recognition. In: Proc. - IEEE Winter Conf. Appl. Comput. Vis. pp. 889\u2013897 (2021)","DOI":"10.1109\/WACV48630.2021.00093"},{"key":"1619_CR56","first-page":"11940","volume":"34","author":"M Ren","year":"2020","unstructured":"Ren, M., Wang, Y., Sun, Z., Tan, T.: Dynamic graph representation for occlusion handling in biometrics. Proc AAAI Conf Artif Intell AAAI. 34, 11940\u201311947 (2020)","journal-title":"Proc AAAI Conf Artif Intell AAAI."},{"key":"1619_CR57","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al: An image is worth 16x16 words: Transformers for image recognition at scale. In: Int. Conf. Learn. Represent. pp. 1\u201321 (2021)"},{"key":"1619_CR58","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: Int. Conf. Mach. Learn., ICML. pp. 10347\u201310357 (2021)"},{"key":"1619_CR59","doi-asserted-by":"crossref","unstructured":"Fang, J., Xie, L., Wang, X., Zhang, X., Liu, W., Tian, Q.: Msg-transformer: Exchanging local spatial information by manipulating messenger tokens. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 12063\u201312072 (2022)","DOI":"10.1109\/CVPR52688.2022.01175"},{"key":"1619_CR60","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.future.2020.01.056","volume":"107","author":"S Adamovi\u0107","year":"2020","unstructured":"Adamovi\u0107, S., Mi\u0161kovic, V., Ma\u010dek, N., Milosavljevi\u0107, M., \u0160arac, M., Sara\u010devi\u0107, M., Gnjatovi\u0107, M.: An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Futur. Gener. Comp. Syst. 107, 144\u2013157 (2020)","journal-title":"Futur. Gener. Comp. Syst."},{"key":"1619_CR61","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, S., Jia, J.: Jigsaw clustering for unsupervised visual representation learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 11526\u201311535 (2021)","DOI":"10.1109\/CVPR46437.2021.01136"},{"key":"1619_CR62","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Int. Conf. Mach. Learn., ICML. pp. 1597\u20131607 (2020)"},{"key":"1619_CR63","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"1619_CR64","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1007\/s11263-018-01142-4","volume":"127","author":"Y Wen","year":"2019","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A comprehensive study on center loss for deep face recognition. Int. J. Comput. Vis. 127, 668\u2013683 (2019)","journal-title":"Int. J. Comput. Vis."},{"key":"1619_CR65","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Eur. Conf. Comput. Vis. pp. 3\u201319 (2018)","DOI":"10.1007\/s11263-019-01198-w"},{"key":"1619_CR66","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Int. Conf. Mach. Learn., ICML. pp. 448\u2013456 (2015)"},{"key":"1619_CR67","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022 (2016)"},{"key":"1619_CR68","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Int. Conf. Comput. Vis. pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"1619_CR69","doi-asserted-by":"crossref","unstructured":"Nuriel, O., Benaim, S., Wolf, L.: Permuted adain: Reducing the bias towards global statistics in image classification. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 9482\u20139491 (2021)","DOI":"10.1109\/CVPR46437.2021.00936"},{"key":"1619_CR70","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1109\/TMM.2023.3311143","volume":"26","author":"Z Zhao","year":"2024","unstructured":"Zhao, Z., Liu, B., Lu, Y., Chu, Q., Yu, N., Chen, C.W.: Joint identity-aware mixstyle and graph-enhanced prototype for clothes-changing person re-identification. IEEE Trans. Multimed 26, 3457\u20133468 (2024)","journal-title":"IEEE Trans. Multimed"},{"key":"1619_CR71","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Mixstyle neural networks for domain generalization and adaptation. Int. J. Comput. Vis. pp 1\u201315 (2023)","DOI":"10.1007\/s11263-023-01913-8"},{"key":"1619_CR72","doi-asserted-by":"crossref","unstructured":"Magris, M., Iosifidis, A.: Bayesian learning for neural networks: an algorithmic survey. Artif. Intell. Rev. pp 1\u201351 (2023)","DOI":"10.1007\/s10462-023-10443-1"},{"key":"1619_CR73","doi-asserted-by":"crossref","unstructured":"Chang, J., Lan, Z., Cheng, C., Wei, Y.: Data uncertainty learning in face recognition. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5710\u20135719 (2020)","DOI":"10.1109\/CVPR42600.2020.00575"},{"key":"1619_CR74","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Int. Conf. Learn. Represent. pp. 1\u201314 (2014)"},{"key":"1619_CR75","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: Int. Conf. Mach. Learn., ICML. pp. 1613\u20131622 (2015)"},{"key":"1619_CR76","doi-asserted-by":"crossref","unstructured":"Zhou, K., Loy, C.C., Liu, Z.: Semi-supervised domain generalization with stochastic stylematch. Int. J. Comput. Vis. pp 1\u201311 (2023)","DOI":"10.1007\/s11263-023-01821-x"},{"key":"1619_CR77","doi-asserted-by":"crossref","unstructured":"Li, P., Xie, J., Wang, Q., Gao, Z.: Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 947\u2013955 (2018)","DOI":"10.1109\/CVPR.2018.00105"},{"key":"1619_CR78","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 6924\u20136932 (2017)","DOI":"10.1109\/CVPR.2017.437"},{"key":"1619_CR79","doi-asserted-by":"crossref","unstructured":"Lu, Z., Yang, Y., Zhu, X., Liu, C., Song, Y.-Z., Xiang, T.: Stochastic classifiers for unsupervised domain adaptation. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 9111\u20139120 (2020)","DOI":"10.1109\/CVPR42600.2020.00913"},{"key":"1619_CR80","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv:1703.07737 (2017)"},{"issue":"5","key":"1619_CR81","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TPAMI.2009.59","volume":"32","author":"PJ Phillips","year":"2009","unstructured":"Phillips, P.J., Scruggs, W.T., O\u2019Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: Frvt 2006 and ice 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 831\u2013846 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR82","unstructured":"Biometrics Ideal Test. CASIA.v4 Database. Accessed: 2002. . Available: http:\/\/biometrics.idealtest.org"},{"issue":"2","key":"1619_CR83","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.imavis.2009.04.010","volume":"28","author":"P Li","year":"2010","unstructured":"Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image Vis. Comput. 28(2), 246\u2013253 (2010)","journal-title":"Image Vis. Comput."},{"key":"1619_CR84","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inform. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1619_CR85","doi-asserted-by":"crossref","unstructured":"Gatys, L.A.: A neural algorithm of artistic style. arXiv:1508.06576 (2015)","DOI":"10.1167\/16.12.326"},{"key":"1619_CR86","doi-asserted-by":"crossref","unstructured":"Liu, S., Lin, T., He, D., Li, F., Wang, M., Li, X., Sun, Z., Li, Q., Ding, E.: Adaattn: Revisit attention mechanism in arbitrary neural style transfer. In: Int. Conf. Comput. Vis. pp. 6649\u20136658 (2021)","DOI":"10.1109\/ICCV48922.2021.00658"},{"issue":"10","key":"1619_CR87","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1109\/TPAMI.2021.3087709","volume":"44","author":"J Deng","year":"2022","unstructured":"Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 5962\u20135979 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR88","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TIFS.2022.3221897","volume":"18","author":"J Wei","year":"2023","unstructured":"Wei, J., Wang, Y., Huang, H., He, R., Sun, Z., Gao, X.: Contextual measures for iris recognition. IEEE Trans. Inf. Forensic Secur. 18, 57\u201370 (2023)","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"1619_CR89","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"1619_CR90","doi-asserted-by":"crossref","unstructured":"Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., Guo, B.: Cswin transformer: A general vision transformer backbone with cross-shaped windows. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 12124\u201312134 (2022)","DOI":"10.1109\/CVPR52688.2022.01181"},{"issue":"10","key":"1619_CR91","doi-asserted-by":"crossref","first-page":"12581","DOI":"10.1109\/TPAMI.2023.3282631","volume":"45","author":"K Li","year":"2023","unstructured":"Li, K., Wang, Y., Zhang, J., Gao, P., Song, G., Liu, Y., Li, H., Qiao, Y.: Uniformer: unifying convolution and self-attention for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(10), 12581\u201312600 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR92","first-page":"2344","volume":"36","author":"C Tang","year":"2022","unstructured":"Tang, C., Zhao, Y., Wang, G., Luo, C., Xie, W., Zeng, W.: Sparse mlp for image recognition: is self-attention really necessary? Proc AAAI Conf Artif Intell AAAI. 36, 2344\u20132351 (2022)","journal-title":"Proc AAAI Conf Artif Intell AAAI."},{"issue":"01","key":"1619_CR93","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TPAMI.2022.3145427","volume":"45","author":"Q Hou","year":"2023","unstructured":"Hou, Q., Jiang, Z., Yuan, L., Cheng, M.-M., Yan, S., Feng, J.: Vision permutator: a permutable mlp-like architecture for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(01), 1328\u20131334 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1619_CR94","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336\u2013359 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"1619_CR95","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1619_CR96","first-page":"1","volume":"36","author":"K Yi","year":"2024","unstructured":"Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., Niu, Z.: Frequency-domain mlps are more effective learners in time series forecasting. Adv. Neural Inform. Process. Syst. 36, 1\u201324 (2024)","journal-title":"Adv. Neural Inform. Process. Syst."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01619-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01619-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01619-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T11:07:46Z","timestamp":1740740866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01619-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,16]]},"references-count":96,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1619"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01619-y","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,16]]},"assertion":[{"value":"26 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article contains no studies with human participants and animals performed by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"18"}}